Some basic concepts of deep learning - an introductory tutorial

An introductory tutorial on deep learning concepts

Deep learning is an artificial intelligence technology with a wide range of applications, including image recognition, speech recognition, natural language processing, recommendation systems, and more. This tutorial will introduce the basic concepts and common models of deep learning.

basic concept

Neural Networks

A neural network is a graphical model consisting of multiple nodes. They are usually organized as a series of layers, each layer applies some operation on the input and outputs to the next layer.

forward propagation

Forward propagation refers to the process of passing input data through different layers in a neural network and calculating the output value layer by layer. In deep learning, forward propagation is one of the key steps in building models and making predictions.

backpropagation

Backpropagation is an algorithm used to train neural networks. In backpropagation, we first feed an example through the network and compute the output. We then compute a loss function based on the difference between the actual output and the desired output, which in turn is used to update the parameters of each node in the network.

Weights and Bias

Weights and biases are parameters in neural networks that are used to adjust the strength and offset of connections between neurons.

Activation function:

The activation function is a nonlinear function in the neural network, which is used to convert the input signal into the output signal and introduce the nonlinear characteristics of the neural network.

loss function

Loss functions are used to measure the difference between the model's predicted output and the true label, and help the model tune parameters to improve performance.

optimizer

An optimizer is an algorithm for optimizing a loss function. The optimizer tries to find a set of node parameters such that the loss function is minimized.

batch processing

Batching is a method of training neural networks in which multiple training examples are processed together at a time. This speeds up training and makes the network more stable when estimating gradients.

Commonly used models

convolutional neural network

A convolutional neural network is a type of neural network suitable for processing two-dimensional image data.
CNN (Convolutional Neural Network) is a neural network widely used in the field of image and speech processing. Its basic concepts include:

1. Convolutional layer: The convolutional layer is the core layer in CNN. Through a series of convolution operations, it extracts local features in the image and generates multiple feature maps.
2. Pooling layer: The pooling layer is used to downsample the feature map, thereby reducing the amount of computation and overfitting problems. Common pooling methods include maximum pooling and average pooling.
3. Convolution kernel: The convolution kernel is a parameter in the convolution layer, which is used for convolution operation, usually a small matrix, and each convolution kernel can extract a specific type of feature.
4. Padding: Padding is adding extra pixels around the edges of the input image so that the convolution kernel can cover the edges of the image. Padding can change the size of the feature map after convolution.
5. Stride: The stride is the distance the convolution kernel moves on the image, and a larger stride will result in a smaller output feature map size.
6. Fully connected layer: The fully connected layer is usually added after the convolutional layer, transforms the feature map output by the convolutional layer into a vector, and maps it to the output of the classification or regression task.
7. Activation function: The activation function is added between the convolutional layer and the fully connected layer, which nonlinearly introduces the nonlinear characteristics of the neural network. Common activation functions include ReLU and sigmoid.

CNN continuously extracts and compresses features through convolution and pooling layers, uses multi-layer convolution and fully connected layers to extract and classify advanced features, and has good performance in image processing and visual tasks.

recurrent neural network

RNN refers to Recurrent Neural Network, which is a neural network model widely used in the field of sequence data processing. It has a cyclic connection structure, which can use the previous state information to process the input of the current time step and output the corresponding output result. In the field of deep learning, RNN has been widely used in natural language processing, speech recognition, time series analysis, video processing and other fields.
RNN (Recurrent Neural Network) is a neural network widely used in the field of sequence data processing. Its basic concepts include:

Circulation unit (cell): The circulation unit is the core unit in RNN, which can save the state of the current input and the previous input, and output the current hidden state. Common recurrent units include LSTM and GRU based structures.

• Sequence input: RNN can accept input sequences of arbitrary length, unlike traditional neural networks that require fixed-size inputs.
• Sequence output: Depending on the task of the model, RNNs can produce sequence outputs of varying lengths, such as a single predicted value, a series of predictions, or generate new sequence data.
• Time step (time step): In RNN, each input data and output data are associated with a time step, and the time step can be understood as the index of the input sequence or output sequence.
• Hidden state: A hidden state is a vector with sequence information held in a recurrent unit, which can be passed along the sequence, thereby capturing long-term dependencies in the sequence.
• Bidirectional Recurrent Neural Network: Bidirectional RNN (BRNN) usually contains two recurrent layers, which process the input sequence along the forward and reverse directions respectively, which can better capture the interdependence in the sequence.
• Gradient disappearance and explosion: When training RNN, the problem of gradient disappearance and explosion will inevitably occur, and gradient clipping and other methods need to be used to stabilize the training process.

RNN learns dependencies between sequences by processing serialized data, such as time series and natural language, and implements various tasks in sequences, such as classification, language model, translation, and sequence generation.

long short-term memory network

LSTM (Long Short-Term Memory, long-short-term memory network) is a special recurrent neural network (RNN), which was proposed by Hochreiter and Schmidhuber in 1997, and is mainly used to solve the problem of gradient disappearance or gradient explosion in conventional RNN.

The core idea of ​​LSTM is to control the flow of information in the network through the gating mechanism. The gating mechanism mainly includes forgetting gate, input gate and output gate. Among them, the forget gate controls when the previous state should be "forgotten", the input gate controls how new information is added to the current state, and the output gate controls which parts of the state are selected for output.

LSTM includes a memory cell and three gates: input gate, forget gate and output gate. Through proper gate control, LSTM can retain past information , to selectively drop or add new information. This makes LSTMs excellent at processing sequence data that requires long-term dependencies.

In short, LSTM is a neural network with long-term memory capability. It controls the update and flow of information status through a gating mechanism, which can well solve the problem of gradient disappearance/explosion in traditional RNN. It is widely used in natural language processing, Speech recognition, video analysis and other fields.

autoencoder

Autoencoders are a type of neural network that can be used for dimensionality reduction or feature extraction. An autoencoder consists of an encoder, which converts the input into a low-dimensional representation, and a decoder, which converts it back to the original dimension.
Autoencoder is an unsupervised learning neural network model for tasks such as data compression, feature extraction, and data denoising. Its basic concepts include:

• Encoder: The encoder compresses the input data into a low-dimensional representation and outputs the encoder output value.
• Decoder: The decoder reconstructs the original data by receiving the output of the encoder.
• Loss function: The loss function is used to measure the difference between the decoder output and the original data. Common loss functions include square error and cross entropy.
• Bottleneck layer: The output of the encoder is usually restricted to a low-dimensional space, called the bottleneck layer. This helps to observe and identify good features of the data.
• Random noise (Noise): The autoencoder can add random noise during the training process, thereby improving the robustness and generalization ability of the model.
• Variational Autoencoder (VAE): A variational autoencoder is a special kind of autoencoder that can learn the distribution of latent variables and sample new data during decoding.

Autoencoders are often used in data compression, denoising, feature extraction, image generation and other tasks. With an autoencoder, we learn low-dimensional representations in the data, and an encoder and decoder that can reconstruct the original data after compression.

Generative Adversarial Networks

A generative adversarial network is a type of neural network that can generate realistic images. They consist of a generator, which generates images, and a discriminator, which decides which images are real.
Generative Adversarial Networks (GAN) is an unsupervised neural network model for generating new data with fidelity. The basic concepts of GAN include:

1. Generator: The generator is the core part of GAN, which takes a random noise vector and converts it into synthetic data similar to real data.
2. Discriminator: The discriminator is a binary classifier used to distinguish the data generated by the generator from the real data.
3. Adversarial learning: GAN adopts the idea of ​​confrontational learning. By continuously iteratively training the generator and the discriminator, the data generated by the generator is more realistic, and the discriminator can more accurately distinguish between real data and synthetic data.
4. Loss function: There are two loss functions in GAN, one is the loss function of the generator, and the other is the loss function of the discriminator. Among them, the generator improves the fidelity by minimizing the error of the discriminator on the generated samples, and the discriminator improves the discrimination accuracy by minimizing the difference between real samples and generated samples.
5. Random noise (Noise): GAN must input a random noise vector, which is the potential space of the generator, which can control how the generator converts noise into output.
6. Mode Collapse (Mode Collapse): A major problem in the GAN training process is mode collapse, that is, the generator cannot generate all the modes of the data distribution, but only some of them.

GAN has a wide range of applications in the fields of image and speech generation for generating high-quality and fidelity data, and is also an important research direction in the field of deep learning. The principle and implementation of GAN are relatively complicated, and it needs to be adjusted and improved appropriately for specific problems.

Using Deep Learning in Practice

Here are some points to keep in mind when using deep learning in practice:

• Prepare dataset
• choose the appropriate model
• Choose an optimizer and hyperparameters
• training model
• Evaluate model performance

data preprocessing

Before training a deep learning model, data must be preprocessed into an appropriate format. This usually includes:

• normalized data
• Scale and normalize the data
• Apply one-hot encoding to categorical variables
• Identify data splits for training, validation, and testing

supervised learning model

A supervised learning model is a model that uses labeled data for training. In supervised learning, each training example contains an input and corresponding desired output. Supervised learning models include:

• linear regression
• logistic regression
• decision tree
• random forest
• Support Vector Machines
• Neural Networks

unsupervised learning model

An unsupervised learning model is one that is trained without labeled data. Unsupervised learning models include:

• clustering
• principal component analysis

Gradient Descent

Gradient descent is an optimization algorithm for training deep learning models. It is based on the backpropagation algorithm, which updates the parameters by computing the gradient of the model parameters and multiplying them with a certain learning rate. Gradient descent methods include:

• batch gradient descent
• stochastic gradient descent
• Mini-batch gradient descent

hyperparameter tuning

When training a deep learning model, it is very important to choose the correct hyperparameters. Hyperparameters include:

• learning rate
• batch size
• Regularization parameter
• Depth and Width of Neural Networks
• activation function

Tuning of hyperparameters can be done manually or using automated tuning methods such as grid search and random search.

model evaluation

After training a deep learning model, model evaluation is required. Model evaluation includes:

• Calculate the loss function
• Calculate accuracy, precision, recall and F1-score and other indicators
• Draw ROC curve and precision-recall curve

model deployment

Deploying a trained deep learning model to a production environment requires consideration of the following factors:

• Store and load models
• Speed ​​of making predictions on new data
• Data Privacy and Security
• Determine when to retrain the model

in conclusion

Deep learning is a powerful artificial intelligence technique with a wide range of applications. It involves many concepts and models, but with careful preparation and practice, you can learn how to implement deep learning models. By studying this tutorial, you can master the basic concepts of deep learning and start building your own deep learning models.